Systems and methods for mapping real estate to real estate seeker preferences

ABSTRACT

Systems and methods are described that provide for determining a relative non-monetary value of real estate properties and translating seeking users&#39; real estate needs into user-specific criteria that can be incorporated in searches and optimize real estate search results based on the seeking users&#39; needs for both real estate seeking users and real estate offering users. Seeking users may be evaluated for user-specific needs and receiver recommendations for real estate to purchase, lease, use, or occupy for highest and best use of available real estate. Furthermore, real estate matching seeking users&#39; needs may be presented to an offering user. The offering user may update any real estate listings to match the needs of the seeking user as well as needs of other seeking users to target specific users or gain more interested users.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.17/365,132, filed Jul. 1, 2021, and entitled “SYSTEMS AND METHODS FORMAPPING REAL ESTATE TO REAL ESTATE SEEKER PREFERENCES” (“the '132application”). The '132 application claims priority benefit of U.S.Provisional Patent Application No. 63/705,545, filed Jul. 3, 2020, andentitled “REAL ESTATE RELATIONSHIP MANAGER.” The identifiedearlier-filed patent applications are hereby incorporated by referencein their entirety into the present application.

BACKGROUND 1. Field

Embodiments of the invention generally relate to real estate, and moreparticularly to assisting users interested in a transaction thatachieves highest and best use of real estate property.

2. Related Art

Typically, real estate professionals employ explicit needs of a sellerby listing and marketing a real estate property to a pool of unknownbuyers. Marketing consists of enumerating the property by explicit datasuch as type, size, price, age, construction, and other typical featureswith some “puffing” written in by the real estate professional. Realestate professionals employ explicit needs of a buyer gained throughinterviewing a buyer to initiate searches of real estate listings andtypically provide electronic reports from automated real estate listingsearches. Buyers and agents search for suitable properties by searchingthe enumerated features, and the buyers may make decisions about viewingproperties based on location, pictures, or the “puffing” statements.

Typical online real estate companies provide the same model as describedabove, on the internet. The online companies provide users withcapabilities to search real estate listings based on limitedstandardized requirement choices entered by the user. Online real estatecompanies retain the user's information and gain additional estimatedlevel of interest by use of a user's internet browsing activitiessometimes without the user's explicit awareness. Online real estatecompanies then interact with the customer via website and phone appalerts, emails, texts, and web browsing advertisements. The user issubjected to a high volume of unsolicited online interaction for thegoal of providing real estate professionals and real estate relatedcompanies with referrals or other information that can be monetized.Users are unable to control the extent of the monitoring of theirbrowsing behavior or other data collected on them which has led to anincreasing resistance and negative feeling about the loss of privacy andthe intrusive nature of repetitive advertising. This in turn has led toan increasing user pushback against providing online real estatecompanies with personally identifiable information that could make thema target of undesirable advertising and junk mail instigated by onlinereal estate companies, their affiliates, and companies that purchasedtheir data. The net result is that online real estate companies fail toserve the best interest of users. Furthermore, only limited tools areavailable to users to assess whether their current real estate useachieves a highest and best use of real estate or whether there are realestate alternatives on the market that do.

Typical real estate matching systems limit to standardized criteria usedfor all properties. Limiting real estate listing systems to standardizedcriteria used for all properties provides only limited utility forevaluating implicit factors influencing a buyer's decision to engage ina transaction with the seller. Buyers, with or without a real estateprofessional, select standardized criteria found in industry listingservices such as number of bedrooms, number of bathrooms, garage,location, and price. The buyer then receives one or more recommendationsof real estate property listed on industry listing services that matchesor closely matches a buyer's criteria selection. The buyer or theiragent must then evaluate each home for its usefulness, appeal, ordesirability to the buyer. The clear metric is the number of matches ofa home's features to a buyer's search criteria.

Another drawback of existing real estate listing systems is that thevalue of the property is generally expressed in monetary terms such asprice or leasing costs that are based on the estimated highest and bestuse of the land plus any improvements. The price or leasing costs areoffered to everyone equally. However, for most people the real estatevalue is incompatible with the offered price or leasing cost because thereal estate property fails to present a highest and best use for them. Abuyer may rate homes differently based on their needs for highest andbest use; however, the price of the property is constant for everyone.

Another drawback of current real estate listing systems is that thereare no effective methods for determining the value of the property basedon buyers' demand for the property's features. The asking price istherefore most strongly related to the seller's perceived value of theproperty. Some sellers think their house is worth more than what buyersare willing to pay while some buyers are willing to pay more than theseller is asking. Overpricing has unintended punitive results for theseller from lengthy “days on market” statistics, price adjustments up ordown, or previous listing engagements that did not result in a sale.Underpricing may lead to a bidding war that may benefit the seller butmay lead to irrational decisions by buyers.

Users interested in selling, buying, leasing, using, or occupying realestate property may have conflicting and concurrent goals for theirfuture real estate relationship. Users want to easily search, identify,and compare real estate offerings in order to obtain the real estateproperty needed for their highest and best use. Users have explicit andimplicit needs that they try to meet with their real estate search thatare not met with the current real estate systems. Examples of explicitneeds are a specific city or area, proximity to points of interest,price range, return on investment, potential risk factors and homefeatures such as size, style, and age. Implicit needs are opinions andunvoiced priorities that are usually offered or apparent as the personor business considers, views, or compares real estate property indetail. Implicit needs may vary depending on real estate opportunity,such as previously unrealized tradeoffs or a change in priority thatwill be acceptable when presented with real estate comparisons.

What is needed is a platform that incorporates a method for determiningthe relative non-monetary value of real estate properties andtranslating users' real estate needs into user-specific criteria thatcan be incorporated in searches, with multiple streams for criteria,thus enhancing users' personal control. Users are increasinglysophisticated in making decisions on real estate transactions andincreasingly active and willing participants in locating properties ofinterest to them or in marketing properties of interest to others. Theseusers may choose to interact with a method that provides them an abilityto evaluate real estate properties offered for purchase, lease, use, oroccupation for highest and best use without loss of privacy andreceiving a volume of advertisements. A centralized, persistent, andprivate evaluation system may then be capable of providingrecommendations based on a user's explicit and implicit needs that canbenefit the user's objective of achieving highest and best use of realestate property.

SUMMARY

Embodiments of the invention address the above-described need byproviding systems and methods that incorporate novel techniques forproviding users with options for real estate transactions that achievehighest and best real estate use or benefit. A real estate seeker (i.e.,seeking user) may provide data on the user's real estate needs from aplurality of data sources. This user provided data may then becharacterized to determine the user's explicit and implicit needs forreal estate use or benefit and this characterization may be stored in atleast one real estate relationship profile. The at least one real estaterelationship profile may then be used to identify other users offeringreal estate properties (i.e., offering users) that have the highestprobability of achieving the seeking user's real estate relationshipprofile goals. The collection of real estate relationship profiles of aplurality of seeking users may be used by an offering user offering realestate property to identify seeking users that have the highestprobability of meeting the seeking users' real estate relationshipprofile goals.

In particular, a first embodiment is directed to one or morenon-transitory computer readable media storing computer-executableinstructions that, when executed by a processor, performs a method ofdetermining non-monetary value of a real estate transaction and matchingreal estate property to attributes of a real estate seeker. In someembodiments, the method comprises the steps of receiving user datacomprising explicit needs of the real estate seeker, determiningimplicit needs of the real estate seeker, receiving explicit real estatedata indicative of available real estate, determining implicit realestate data, comparing real estate data indicative of the available realestate to the explicit needs and the implicit needs of the real estateseeker, determining a plurality of real estate property options based onthe comparison, and causing display of the plurality of real estateoptions to the real estate seeker.

A second embodiment is directed to a method of determining non-monetaryvalue of a real estate transaction and matching real estate property toattributes of a real estate seeker. In some embodiments, the methodcomprises the steps of receiving user data comprising explicit needs ofthe real estate seeker, determining implicit needs of the real estateseeker, receiving explicit real estate data indicative of available realestate, determining implicit real estate data, comparing real estatedata indicative of the available real estate to the explicit needs andthe implicit needs of the real estate seeker, determining a plurality ofreal estate property options based on the comparison, and causingdisplay of the plurality of real estate options to the real estateseeker.

A third embodiment is directed to a system for determining non-monetaryvalue of a real estate transaction and matching real estate property toattributes of a real estate seeker, the system comprising a data store,a processor, one or more non-transitory computer readable media storingcomputer-executable instructions that, when executed by the processor,performs a method of determining the non-monetary value of the realestate transaction and matching the real estate property to theattributes of the real estate seeker. In some embodiments, the methodcomprises the steps of receiving user data comprising explicit needs ofthe real estate seeker, determining implicit needs of the real estateseeker, receiving real estate data indicative of available real estate,comparing the real estate data indicative of the available real estateto the explicit needs and the implicit needs of the real estate seeker,determining a plurality of real estate property options based on thecomparison, and causing display of the plurality of real estate propertyoptions to the real estate seeker.

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the detaileddescription. This summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter. Other aspectsand advantages of the current invention will be apparent from thefollowing detailed description of the embodiments and the accompanyingdrawing figures.

BRIEF DESCRIPTIONS OF THE DRAWING FEATURES

Embodiments of the invention are described in detail below withreference to the attached drawing figures, wherein:

FIG. 1 depicts an exemplary hardware platform for certain embodiments ofthe invention;

FIG. 2 depicts an exemplary process for evaluating a potentialtransaction between a seeking user and an offering user;

FIG. 3 depicts a characterization of real estate needs of a user seekinga real estate transaction;

FIG. 4 depicts an exemplary process for establishing a user profile;

FIG. 5 depicts an embodiment of real estate relationships in a userprofile;

FIG. 6 depicts an exemplary process for identifying attributes based ondescription;

FIG. 7 depicts exemplary proximity attributes of a real estate property;

FIG. 8 depicts an exemplary process for identifying and classifyingdesirable and undesirable real estate attributes from images;

FIG. 9 depicts an exemplary process of establishing user preferencesweights;

FIG. 10 depicts an exemplary process of establishing, refining andupdating weights;

FIG. 11 depicts an exemplary process for ranking user preferences;

FIG. 12 depicts an exemplary process for identifying a set of comparablereal estate properties;

FIG. 13 depicts an exemplary process of user review of comparable realestate properties;

FIG. 14 depicts an exemplary process for obtaining real estate data fora real estate property;

FIG. 15 depicts an embodiment of scoring an exemplary real estateproperty;

FIG. 16 depicts an exemplary process for calculating the relativenon-monetary value of a real estate property;

FIG. 17 depicts a process of connecting users seeking and offering realestate transactions;

FIG. 18 depicts an embodiment of matching real estate properties with aseeking user based on relative scores;

FIG. 19 depicts an exemplary process of connecting seeking users tooffering users;

FIG. 20 depicts an embodiment of matching users to a property based onrelative scores;

FIG. 21 depicts an exemplary process of connecting an offering user to aseeking user; and

FIG. 22 shows an exemplary process of generating reports of results.

The drawings do not limit the invention to the specific embodimentsdisclosed and described herein. The drawings are not necessarily toscale, emphasis instead being placed upon clearly illustrating theprinciples of the invention.

DETAILED DESCRIPTION

At a high level, embodiments of the invention provide systems andmethods that determine relative non-monetary value of a real estatetransaction based on a seeking user's goals for highest and best use ofreal estate property. The seeking user's implicit needs and explicitneeds may be compared to available real estate data and optimized toprovide highest and best use for the seeking user. The explicit needsand the implicit needs may provide both monitory and non-monitorybenefits to the seeking user and offering user and any service providerproviding a service to the agent or seller of the real estate. Thetransaction may involve the purchase, lease, use or otherwise occupationof the real estate, and may provide a service that enables the user toachieve highest and best use of the real estate. In some embodiments,the highest and best use of a property may be to sell, lease, or rentthe property.

The seeking user may provide data for evaluating the user'scompatibility with the user's goals for the real estate. The userprovided data may include user data such as, for example, userobjectives for real estate use, the user's real estate history, userselected pictures, user specified preferences, user specifiedconnections such as, for example, specific locations, businesses,entertainment, previous addresses, points of interest, outdoor features,and other explicit or implicit inputs. The user may control how the datais used in the evaluation of offered real estate. In some embodiments,the user may receive offered real estate and update the user supplieddata to adjust the user supplied data to receive real estate offers thatare compatible with the updated user supplied data.

In some embodiments, the systems and methods described herein maygenerally be referred to as a personal real estate system. In someembodiments, the personal real estate system may facilitate interactionwith a seeking user for the collection of factors indicatingcompatibility, desires, expectations, and satisfaction informationexpressed in at least one customized real estate relationship profile.The relationships profile may be processed with additional informationsuch as, for example, real estate related trends, and real estaterelated features. The additional information may improve the results ofa real estate listings searches and offered for a real estate listingsearch that are tested for relevancy and desirability applied to the atleast one customized seeking user's profile during an analysis process.The analysis may be performed for the final goal of facilitating theseeking user decision to pursue a new real estate transaction or to notpursue a new real estate transaction. The seeking user may be one ormore individuals, or an informal or formal group, who desires toevaluate current or future real estate for best and highest use andassociated transactions such as buying, leasing, using, or otherwiseoccupying real estate. The real estate may be any private, public, orcommercial property.

In some embodiments, the personal real estate system facilitates privateand specific interaction with a real estate offering user for thecollection of factors indicating compatibility, desires, expectations,and satisfaction information expressed in one or more seeking users'profiles, processed with additional information, such as real estaterelated trends, and real estate related features. The data may beprocessed to improve the results of the seeking users' real estatelistings search or offered to the seeking users for a real estatelisting search that are tested for relevancy and desirability applied tothe at least one customized profile during an analysis process. Theanalysis may be performed for the final goal of facilitating theoffering user's decision to pursue a new real estate transaction, or tonot pursue a new real estate transaction, or to add, modify, or deleteinformation on the offered real estate transaction. The offering usermay be one or more individuals, or an informal or formal group, whodesires to offer real estate for best and highest use and associatedtransactions such as offering to buy, lease, use, or otherwise occupyreal estate, or to provide services for offering to buy, lease, use, orotherwise occupy real estate.

In some embodiments, the personal real estate system facilitatesevaluating the compatibility of the user's current ownership, lease, useor occupation of real estate with the user's goals for highest and bestuse of real estate. In this embodiment, the seeking user and offeringuser are the same entity, and the system may enable the user to evaluatewhether the user's current ownership, lease, use or occupation of realestate is satisfactory or unsatisfactory, independent of other realestate offered for ownership, lease, use or occupation.

The subject matter of the embodiments of the invention is described indetail below to meet statutory requirements; however, the descriptionitself is not intended to limit the scope of claims. Rather, the claimedsubject matter might be embodied in other ways to include differentsteps or combinations of steps similar to the ones described in thisdocument, in conjunction with other present or future technologies.Minor variations from the description below will be obvious to oneskilled in the art and are intended to be captured within the scope ofthe claimed invention. Terms should not be interpreted as implying anyparticular ordering of various steps described unless the order ofindividual steps is explicitly described.

The following detailed description of embodiments of the inventionreferences the accompanying drawings that illustrate specificembodiments in which the invention can be practiced. The embodiments areintended to describe aspects of the invention in sufficient detail toenable those skilled in the art to practice the invention. Otherembodiments can be utilized, and changes can be made without departingfrom the scope of the invention. The following detailed description is,therefore, not to be taken in a limiting sense. The scope of embodimentsof the invention is defined only by the appended claims, along with thefull scope of equivalents to which such claims are entitled.

In this description, references to “one embodiment,” “an embodiment,” or“embodiments” mean that the feature or features being referred to areincluded in at least one embodiment of the technology. Separatereference to “one embodiment” “an embodiment”, or “embodiments” in thisdescription do not necessarily refer to the same embodiment and are alsonot mutually exclusive unless so stated and/or except as will be readilyapparent to those skilled in the art from the description. For example,a feature, structure, or act described in one embodiment may also beincluded in other embodiments but is not necessarily included. Thus, thetechnology can include a variety of combinations and/or integrations ofthe embodiments described herein.

Turning first to FIG. 1 , an exemplary hardware platform for certainembodiments of the invention is depicted. Computer 102 can be a desktopcomputer, a laptop computer, a server computer, a mobile device such asa smartphone or tablet, or any other form factor of general- orspecial-purpose computing device. Depicted with computer 102 are severalcomponents, for illustrative purposes. In some embodiments, certaincomponents may be arranged differently or absent. Additional componentsmay also be present. Included in computer 102 is system bus 104, wherebyother components of computer 102 can communicate with each other. Incertain embodiments, there may be multiple busses or components maycommunicate with each other directly. Connected to system bus 104 iscentral processing unit (CPU) 106. Also attached to system bus 104 areone or more random-access memory (RAM) modules 108. Also attached tosystem bus 104 is graphics card 110. In some embodiments, graphics card110 may not be a physically separate card, but rather may be integratedinto the motherboard or the CPU 106. In some embodiments, graphics card110 has a separate graphics-processing unit (GPU) 112, which can be usedfor graphics processing or for general purpose computing (GPGPU). Alsoon graphics card 110 is GPU memory 114. Connected (directly orindirectly) to graphics card 110 is display 116 for user interaction. Insome embodiments no display is present, while in others it is integratedinto computer 102. Similarly, peripherals such as keyboard 118 and mouse120 are connected to system bus 104. Like display 116, these peripheralsmay be integrated into computer 102 or absent. Also connected to systembus 104 is local storage 122, which may be any form of computer-readablemedia and may be internally installed in computer 102 or externally andremovably attached.

Computer-readable media include both volatile and nonvolatile media,removable and nonremovable media, and contemplate media readable by adatabase. For example, computer-readable media include (but are notlimited to) RAM, ROM, EEPROM, flash memory or other memory technology,CD-ROM, digital versatile discs (DVD), holographic media or otheroptical disc storage, magnetic cassettes, magnetic tape, magnetic diskstorage, and other magnetic storage devices. These technologies canstore data temporarily or permanently. However, unless explicitlyspecified otherwise, the term “computer-readable media” should not beconstrued to include physical, but transitory, forms of signaltransmission such as radio broadcasts, electrical signals through awire, or light pulses through a fiber-optic cable. Examples of storedinformation include computer-useable instructions, data structures,program modules, and other data representations.

Finally, in some embodiments, network interface card (NIC) 124 is alsooptionally attached to system bus 104 and allows computer 102 tocommunicate over a network such as network 126. NIC 124 can be any formof network interface known in the art, such as Ethernet, ATM, fiber,Bluetooth, or Wi-Fi (i.e., the IEEE 802.11 family of standards). NIC 124connects computer 102 to local network 126, which may also include oneor more other computers, such as computer 128, and network storage, suchas data store 130. Generally, a data store such as data store 130 may beany repository from which information can be stored and retrieved asneeded. Examples of data stores include relational or object-orienteddatabases, spreadsheets, file systems, flat files, directory servicessuch as LDAP and Active Directory, or email storage systems. A datastore may be accessible via a complex API (such as, for example,Structured Query Language), a simple API providing only read, write, andseek operations, or any level of complexity in between. Some data storesmay additionally provide management functions for data sets storedtherein such as backup or versioning. Data stores can be local to asingle computer such as computer 128, accessible on a local network suchas local network 126, or remotely accessible over Internet 132. Localnetwork 126 is in turn connected to Internet 132, which connects manynetworks such as local network 126, remote network 134 or directlyattached computers such as computer 136. In some embodiments, computer102 can itself be directly connected to Internet 132.

FIG. 2 depicts an exemplary process overview of determining a relativenon-monetary value of a real estate transaction to seeking user 204seeking a transaction with offering user 212 offering a transactionrepresented generally by the reference numeral 200. In some embodiments,seeking user 204 may prepare a custom profile including the usersupplied data as described above. The custom profile may includeexplicit needs stated by seeking user 204. In some embodiments, thecustom profile may include the user data comprising fundamental realestate objective, a real estate history, a location of current anddesired community connections, a location of current and desired needsfor amenities, desirable interior features of real estate property,desirable exterior features of real estate property, and acceptable andunacceptable material facts. Furthermore, the custom profile may includeany information that may aid in selecting real estate to meet thehighest and best use of the real estate for seeking user 204.

At step 202, explicit needs and implicit needs for real estate use andbenefit of the at least one seeking user 204 may be characterized from aplurality of data sources. A process for characterizing the needs ofseeking user 204 is depicted in FIG. 3 and described in detail below. Atstep 206, data indicative of said needs of seeking user 204 may beobtained from a plurality of data sources. At step 208, seeking user 204may add, delete, or select which data indicative of the explicit needsand the implicit needs to include in the characterization. At step 210,a plurality of real estate transactions may be offered to seeking user204 based on the explicit needs and the implicit needs. At step 214, therelative non-monetary value of a real estate transaction may becalculated and expressed as the probability that the real estatetransaction achieves the seeking user's highest and best real estate useor benefit, where the highest and best real estate use is an optimizedvalue or values indicative of how well the real estate is associatedwith the user input data (e.g., the implicit needs and the explicitneeds).

FIG. 3 depicts an exemplary process for characterizing the real estateneeds of seeking user 204 seeking a real estate transaction representedgenerally by the reference numeral 300. Initially, at step 302, the userdata is collected and stored in a user profile. The user data may be anyinformation associate with a user profile such as, for example, identityinformation, tax information, credit information, demographicinformation, location information, and the like. Furthermore, the userprofile may include any real estate needs, attributes, and any otherinformation that may be used to determine the highest and best realestate for use by seeking user 204 as described herein. The user datamay be used by the personal real estate system to compare and matchavailable real estate with seeking user 204 to provide the best andhighest use for the real estate and provide the highest satisfaction toseeking user 204.

At step 304, seeking user's real estate needs (i.e., explicit needs andimplicit needs) may be identified from the user data collected fromseeking user 204. Examples of an explicit need may be a 4-bedroomdetached house, 2.5 bathrooms, a two-car garage, a location in aparticular area, and any other information that may be explicitly neededand provided by seeking user 204. Examples of an implicit need may be aquiet private outdoor area, short and easy commute, sun exposure, maturelandscaping, and any other need that may be implicitly evaluated forseeking user 204.

At step 306, attributes of each real estate need of seeking user 204 maybe identified. In some embodiments, examples of an attribute may beheated square footage, number of bedrooms, land area, proximity to theairport, distance to nearest neighbor, distance to downtown, distance toshopping, and any other relevant attribute that may contribute to and beclassified as one or more of proximity, quality of life, comfort,appeal, effective age, and other. Furthermore, attributes may bedesignated as primary attributes and secondary attributes based onimportance to seeking user 204 and based on dependencies. In someembodiments, the dependent attributes may be categorized as secondaryattributes.

At step 308, dependencies between attributes of the user's real estateneeds may be identified. An example of an attribute dependency is theattribute “quiet” is dependent on a wide selection of other attributeseither alone or in combination such as, for example, proximity to amulti-lane, high speed road, an airport flight path, or the existence ofan installed privacy fence. Similarly, seeking user 204 may have a pet,such as a dog. Proximity to a veterinarian may be analyzed as well asproximity to the neighbors and a probability of a Homeowner'sAssociation (HOA) may also be determined.

At step 310, explicit preferences and implicit preferences of seekinguser 204 for each attribute may be identified. In some embodiments, anexample of an explicit preference may be a requirement for a garage. Anexample of an implicit preference is for a garage that has a connectingdoor to the house on the ground floor. The connected door to the garagemay be an example of the dependencies between attributes and thepreferences built into those dependencies.

At step 312, at least one pass-fail criterion may be assigned to eachattribute based on seeking user's implicit preferences and explicitpreferences, and, in some embodiments, the criterion may be representedas a value or a probability distribution. In an exemplary embodiment, apass-fail criterion represented as a value may be a preference that ahome has a two-car garage. A home with a one car garage may fail thiscriterion whereas a home with a garage that accommodates two or morepasses. This is a binary pass-fail criterion based on a binary yes or noevaluation. In some embodiments, the binary evaluation may apply tonon-binary attributes. For example, a non-binary attribute may representa size of the property. Seeking user 204 may prefer a lot of land thatis between 0.5 and 1.0 acres. The lot of land may be evaluated such thatanything 0.5 to 1.0 acres is classified as 1 (“yes”) or 0 (“no”).Alternatively, the pass-fail criterion may be represented as aprobability distribution. A property with land size between 0.5 and 1.0would pass (get a score of 1 for this criterion), but a property with aland size just outside this range should not be discarded and insteadget a slightly lower score for this criterion. Property that has a landsize well outside of the preferred range gets a very low score or ascore of zero for this criterion.

At step 314, a weight may be assigned to each attribute score,reflecting how much one attribute is preferred over another. Forexample, seeking user 204 may indicate which attributes are moredesirable on a scale of 1-10, indicating least desirable to mostdesirable, or any other classification method. In some embodiments,weights may be applied automatically based on the user data, historicalpersonal data of seeking user 204, on crowd sourced data of similarattributes, or any combination thereof. In some embodiments, seekinguser 204 may update the user data at any time, and the weights may beupdated indicative of the updated data.

The embodiment presented in FIG. 3 presents the collection of factorsindicating compatibility, desires, expectations, and satisfactioninformation expressed in one or more user profiles, processed withadditional information that may be received from seeking user 204 andincluded in the user data. Exemplary additional information may includethe user's opinions about real estate related trends and any other realestate related features. The collection of factors may be used toimprove the results of real estate listings searches, or offers toseeking user 204 for a real estate listing search that are tested forrelevancy and desirability applied to the at least one customized user'sprofile during an analysis process for the final goal of facilitating auser's decision to pursue a new real estate transaction or to not pursuea new real estate transaction.

Turning now to FIG. 4 , a schematic of an exemplary process ofestablishing customized user profiles 402 is depicted and referred togenerally by reference numeral 400. In the example shown, real estateprofiler 406 interacts with seeking user 204 through user interface 408to collect relevant information that defines the real estate preferencesof seeking user 204. In some embodiments, user data provided by seekinguser 204 may include individual objectives 410, real estate history 412,albums 414, preferences 416, connections 418, and any other information420 that seeking user 204 provides as user data and stored in customizeduser profiles 402. In some embodiments, the relevant information may beused to define the past, current, and desired real estate relationshipsof seeking user 204 that achieves best and highest real estate use forseeking user 204.

In some embodiments, individual objectives 410 may include, for example,a bigger house, more land, reduced commute time, lower housing expenses,maximized real estate appreciation potential, maximized potential returnon real estate investment, and any other objective that achieves bestand highest real estate use as defined by seeking user 204. Real estatehistory 412 may include addresses of past and current real estate owned,rented, visited, and lived in by seeking user 204. Albums 414 mayinclude real estate exterior images and/or interior images of past andcurrent real estate owned, rented, visited, and lived in by seeking user204. Furthermore, albums 414 may include real estate exterior andinterior images collected through any means by seeking user 204 such as,for example, web browsing, and that seeking user 204 identifies asappealing or unappealing. Preferences 416 may include specificattributes, such as location, real estate architecture, zoning, HOArestrictions, specific material facts, size, price, or cost to use,sewage system, and other real estate related attributes. Connections 418may include workplace, family, friends, religious affiliations, sportsaffiliations, leisure activities, restaurants, stores, charitableorganizations, and other personal connections visited or desire to visitby seeking user 204. The total collection of information 422 provided byseeking user 204 may be at least one piece of information in at leastone category of individual objectives 410, real estate history 412,albums 414, preferences 416, connections 418, and any other information420 that seeking user 204 is willing to provide. In some embodiments,real estate profiler 406 identifies and quantifies implied real estaterelationships of seeking user 204 from the total collection of theinformation 422 provided by seeking user 204 by comparing theinformation contained in a real estate database 424 of past and currentreal estate, a database of customized user profiles such as database ofother user profiles 426, and a database of real estate attributes 428.The database of real estate attributes 428 may be obtained from the realestate database 424 of past and current real estate, the database ofother user profiles 426 of other users, and from open-source data 430comprising any open-source data related to real estate. In someembodiments, information indicative of geographic locations is stored ingeographic feature database (GFD) 432. The geographic locationinformation may be used in determining real estate location and relativedistance to landmarks and is discussed in more detail in reference toFIG. 7 and described below.

In some embodiments, rather than evaluating a plurality of real estateproperties, seeking user 204 may input information indicative of asingle property and the single property may be spontaneously evaluatedbased on the needs of seeking user 204. Furthermore, a GPS location ofseeking user may be tracked and a real estate property may be evaluatedbased on a location of seeking user 204. For example, seeking user 204may be driving through a neighborhood house-hunting, and come across aproperty that seeking user 204 wishes to evaluate. Seeking user 204, mayopen the application and, based on the location of seeking user 204 andthe proximity of seeking user 204 to the listed property, the listedproperty may automatically be evaluated based on the needs of seekinguser 204. As such, any reference to real estate property and anyanalysis of real estate property described herein may include one ormore real estate properties and may be evaluated based on user input oron relative location of seeking user 204 to a property to be evaluated.

Turning now to FIG. 5 , a schematic of an exemplary real estaterelationship profile of a user is depicted and referred to generally byreference numeral 500. In some embodiments, seeking user 204 may provideat least one piece of real estate related user data 504 that can be usedto establish a real estate relationship based on determined intention ofseeking user 204. Real estate related user data 504 may compriseexplicit data such as, for example, a four-bedroom house, at least 2.5bathrooms, a garage, and any other explicit data. Furthermore, realestate related user data 504 may comprise implicit data. For example,seeking user 204 may fill out a questionnaire or provide data in anymanner as user data, and indicate “Exercise is very important to me”which may translate to implied data such as proximity to fitness healthbusinesses, state, local or national parks, greenways, and otherexercise-related attributes. Similarly, implied data may be obtainedfrom images of specific items or situations that can be translated todiscrete elements and reassembled for classification and categorizationsuch as, for example, prioritizing curb appeal based on analysis oflandscaping, front doors, and exterior home colors in pictures. Theexplicit data and implicit data may be categorized. The categories mayhelp seeking user 204 and personal real estate system to prioritize thehighest and best use components for a real estate selection, creating anopportunity for improved results. In some embodiments, the explicit dataand the implicit data may be explicit needs and implicit needs ofseeking user 204.

In some embodiments, past, current, and future real estate relationshipscan be described using data contained in the above-described categories.Positive and negative experiences can be captured by simple “like” or“dislike” submitted by seeking user 204. Furthermore, the implicitrepeated selection of some attributes in the past and current realestate relationships as well as idealized relationships from imageinclusions or other implicit online actions may be categorized.Continuous collection and analysis of a wide group of profiles,marketplace evaluations, sales trends, and other data sources canidentify attributes that are more or less desirable in real estaterelationships based on collected historical data and seeking userassociated data. Data that is related through correlation and obtainedthrough these methods may be assembled in categories, creatinguser-defined requirements (needs) and preferences. The categories can bepre-defined or user titled for meaningful identification (e.g.,Proximity 506, Quality of Life 508, Interior Features 510, ExteriorFeatures 512). One or more of the user categories may then be combinedin a profile to emphasize user priorities, specific attributes, andimplicit desires. The capability to keep a persistent profile or set ofprofiles may allow seeking user 204 to change aspects of their customreal estate relationship needs, keeping up with new trends, homefeatures, life changes, and other significant factors that affect realestate highest and best use. In some embodiments, as interests and needsof seeking user 204 change, seeking user 204 may provide feedback thatmay change the overall attributes database, mapping the evolving usertrends. The updated user data may introduce new attributes, diminishattributes that are losing value, and update the search results overtime. Consequently, the personal real estate system may be updated withthe most recent trends, needs, and preferences for seeking user 204.

FIG. 6 depicts an exemplary process for identifying real estateattributes based on real estate descriptions represented generally bythe reference numeral 600. Initially, at step 602, real estateattributes are received from a plurality of data sources such as, forexample, real estate property listings 604, textual descriptions 606,public information 608, user comments 610, user ratings 612, and otherdescription data 614 that describes real estate properties. For example,Multiple Listing Service (MLS) listings can provide a wide range oftypical attributes such as the number of rooms including the types ofrooms and number of bedrooms, age of the property, construction of theproperty, lot size, location, and any other property attribute. Picturesor images of real estate items such as, for example, a farmhouse sink,tile, and hardwood floors from listings, photographers, and any otherdata source can be processed for real estate attributes. Textualdescriptions 606 are often included in a listing, and in a builder'smarketing information, and can be gained from agents or people viewingthe property and may be processed for attributes. Public information 608like tax records, deed recordings, local municipality publicannouncements, and social media may provide information in text,schematics, pictures, and images that may be processed into real estateattributes such as lot dimensions, location, or associated neighborhoodfeatures. User comments 610 and user ratings 612 may provide informationrelated to real estate attribute desirability or scarcity.

At step 616, the real estate attributes may be analyzed to identifyexplicit attributes 618 and to identify implicit attributes 620.Furthermore, geographic maps and images that show proximity of naturaland man-made features and accurate traffic estimations can provide realestate attributes relative to a location. As described above, anexemplary explicit attribute may be the occurrence of having bedroomswith a value at least 1. An exemplary implicit attribute may be a quietbackyard. The quiet backyard may be an implicit attribute because it maybe evaluated based on other attributes such as, for example, a propertyhaving a privacy fence, proximity to a low traffic and low speed road,and the absence of a nearby airport, thus avoiding flight path noise.

FIG. 7 depicts exemplary proximity attributes of a single real estateproperty in an exemplary environment 700. In exemplary environment 700depicted in FIG. 7 , real estate property 702 includes a sidewalk 704 infront, west neighboring real estate property 706, and east neighboringreal estate property 708, first property 710, second property 712 andthird property 714 across the street, and rear property 716. Real estateproperty 702 is located near first intersection 718 and secondintersection 720 and is near a power line 722 and school grounds 724.Within the vicinity is a 4-lane road 726, a grocery store 728, a park730, and a highway 732. In some embodiments, each proximity attribute isdescribed by primary characteristics of static data such as footprint,height, type, age, and elevation difference with and distance to realestate property 702, and secondary characteristics of temporal data suchas traffic volume, sun exposure, and flooding potential. In someembodiments, the proximity attributes of a real estate property 702 maybe used by the personal real estate system to evaluate if real estateproperty 702 achieves a user's best and highest real estate useaccording to seeking user 204, taking into account seeking user'scomplex criteria for location, proximity to neighbors, avoidance ofpower line easement, proximity to a school, proximity to a park, highwayand grocery access, and acceptable road noise, as well as meeting anyother needs and preferences of seeking user 204.

In some embodiments, GFD 432 stores data that may be used to identify,score, and rank properties based on the needs of seeking user 204. Insome embodiments, data may be obtained from a plurality of data sourcessuch as, for example, listing data (e.g., Multiple Listing Service(MLS)), property records (e.g., parcel data, tax records), landmark data(e.g., shops, attractions, bus stops), line data (e.g., roads, railways,area boundaries), terrain data (e.g., Digital Elevation Maps (DEM),Light Detection and Ranging (LIDAR) data, National Oceanic andAtmospheric Association (NOAA) data), imagery (e.g., satellite imagery,machine learning data sets), and other geographic data sources such asOpen Street Map, Google, ArcGIS, Bing, and the like. In someembodiments, the collected data may be processed and stored in tables asdescribed in more detail below. GFD data may be split into 0.2 by0.2-degree longitude and latitude cells and a file stored for each cell.The processing and storage of data may provide quick and easy accesssuch that the data may be obtained and analyzed offline and, in someembodiments, seeking user 204 may receive updated real estateinformation in about 0.3 seconds at scale. In some embodiments, highvolumes of queries, up to hundreds of thousands, may be performed, andresults may be delivered in about 0.3 seconds.

Further, as stated above, it may be important to have accurategeographic information. The stored data on GFD 432 may be processed tocorrect for incorrect or incomplete addresses from, for example,incorrectly entered addresses, missing house numbers, incorrect zipcode, misspellings, and the like. Furthermore, the data stored in GFD432 may be adjusted data that is adjusted for incorrect or incompletegeospatial coordinates located at a driveway entrance or center of aregion rather than the center of a house. To reduce the noise inincorrect location information a plurality of geographic location datasources may be used, and the plurality of obtained locations may becombined and a median location and standard deviations determined.Furthermore, the error between the plurality of locations may beminimized using statistical and machine learning algorithms. In someembodiments, only data that is the most current among theabove-mentioned data sources may be used.

An exemplary implicit need determination is now described in referenceto FIG. 7 . A user may fill out a questionnaire to obtain informationrelated to implicit needs of an exemplary lifestyle category. Forexample, seeking user 204 may indicate via the questionnaire thatseeking user 204 enjoys outdoor activities and does not like noise. Theanswers to the questionnaire may be mapped to lifestyle categories andstored in a lifestyle table in GFD 432 creating a relationship betweendata elements in the lifestyle table. Lifestyle metrics may becalculated as described below and stored in a second table, a metricstable. To reduce the table data analyzed, only lifestyle elementsindicated via the questionnaire may be analyzed. Furthermore, ifconflicting lifestyle elements exist, the data may be ignored.

In some embodiments, to score implicit needs, proximity of locations oflifestyle elements to real estate property may be determined and storedin the metrics table. For example, the distance between real estateproperty 702 and park 730 may be determined. Furthermore, a number ofparks, or any other lifestyle element, within a half mile, one mile, andfive miles, may be determined and stored. Furthermore, a distancebetween real estate property 702 and highway 732, downtown, airports,railroad tracks, and the like may be determined to score noise. Needsmay be scored based on number within an area and distance to the statedimplicit elements. In some embodiments, only real estate properties withimplicit scores above a predetermined threshold may be presented toseeking user 204. In some embodiments, all scores for each lifestyleelement are combined and weighted. The weights may initially be the sameor different based on the user data. In some embodiments, seeking user204 may submit evaluation of the results and the personal real estatesystem may provide updated results and weights for each attribute basedon feedback of seeking user 204. In some embodiments, the results areautomatically updated each time a user logs into the personal realestate system, when a user provides new user data, and periodically whenthe collection of new real estate changes.

FIG. 8 depicts an exemplary process of identifying and classifyingdesirable and undesirable real estate attributes from images generallyreferenced by numeral 800. In some embodiment, image classifier 802receives input from seeking user 204 identifying examples of desirableand undesirable real estate attributes 806 in real estate related images808 provided by or selected by seeking user 204 in the process ofestablishing customized user profiles 402 described above. In someembodiment, real estate attributes 806 may be extrapolated from sourcessuch as images of the exterior of properties such as, for example,front, sides, and rear of homes, decks, driveways, front entrances, andyards. In some embodiments, real estate attributes 806 may beextrapolated from interior features such as kitchens, built-inappliances, bathrooms, bathroom fixtures, bedrooms, ceiling heights,light fixtures, windows, doors, wall coverings, and floor coverings, andany other real estate related feature that are contained in picturesthat may be selected by or provided by any user or included online.

In some embodiments, attribute desirability estimator 810 may explorethe local real estate listing archive 812 to estimate factors 814 suchas popularity, availability, price effect, and trends that influence thehighest and best use value of seeking user 204. In some embodiment, realestate listing archive 812 includes current and past real estatelistings with current and past real estate listing images as well asrelevant factors such as, for example, initial asking price, days onmarket, sold pricing information, and any other information that can beused to characterize the impact of individual real estate features 806.In some embodiments, real estate listing images are analyzed fordiscrete attributes of interest to the population of users, assigneddesirability factors, and further grouped for statistical use indetermining the impact of individual real estate attributes on realestate value due to demand, price effect, and availability.

In some embodiments of the processes of identifying and classifyingdesirable and undesirable real estate attributes from images, imageclassifier 802 may be a Convolutional Neural Network (CNN) or otherMachine Learning (ML) implementation that is trained on exemplary imagesto recognize and classify real estate attributes without the assistanceof seeking user 204. Desirability and undesirability of real estateattributes from images can be established in a variety of ways with orwithout the assistance of seeking user 204.

FIG. 9 depicts an exemplary process for determining attributepreferences of seeking user 204 seeking a real estate transactionrepresented generally by the reference numeral 900. At step 902, usercriteria are identified based on images. For example, a user can providea picture of a vintage two story farmhouse style home with a sweepingflat lawn and a driveway. If a user supplies multiple images for whichsome features are similar then the similarities can be used to inferwhich features (i.e., attributes) the user finds desirable.

At step 904, the stated priority of specific attributes or impliedpriority of attributes of subjective cues may be identified. A statedpriority may be a priority that seeking user has specifically indicated.For example, seeking user 204 may have indicated an attribute that ahouse has 4 bedrooms is more important to seeking user 204 than anattribute that the house has two stories. Additionally, in someembodiments, the priorities may be implicitly extracted from the usersubmitted data.

At step 906, the desirability of specific attributes with respect toinformation provided by seeking user 204 in regard to context of user,industry, and community trends may be identified. For example, acomparison of the preferences of seeking user 204 to the preferences ofother others may identify users with similar preferences. The comparisonmay also identify that the other users also indicated that farmhousestyle sinks are a desirable attribute. In that case, the attribute of ahouse having a farmhouse style sink has an implied priority over anotherattribute that the other users deemed a lower priority, such as, forexample, stainless appliances.

At step 908, implied connections of attributes to other attributes withimputed desirability may be identified. For example, a multi-lane roadwith a speed limit of over 55 miles an hour and high capacity may beregarded as undesirable for residential users because of imputedcharacteristics of noise, airborne dirt and dust, and unsightly views. Acommercial user may regard the same road as having the desirablecharacteristics of convenient transport, speed for delivery, and a roadnetwork sufficient to accommodate large vehicles. At step 910,attributes may be mapped to groupings descriptive of desirability ofseeking user 204 or the commercial user. Continuing with the previousexample of a multi-lane road, the explicit grouping description “quietoutdoor living” may encompass the attributes of proximity of amulti-lane road and/or a planned expansion to an existing road, trafficspeed, traffic density, privacy fencing or screening, distance of thedwelling to the road, distance between other dwellings, and proximity tocommercial or public buildings.

FIG. 10 depicts an exemplary process of establishing, refining, andupdating weights assigned to real estate relationships in customizeduser profile 1004, generally referenced by the numeral 1000. In someembodiments, preference ranking engine 1002 maps the real estaterelationships stored in the customized user profile 1004 of seeking user204 to attributes in database 1008 and calculates normalized weights ofthe attributes based on preferences stored in a customized user profile1004 of seeking user 204. In some embodiments, customized user profile1004 is the same as seeking user 204 customized user profiles 402described in embodiments above. In the event that seeking user 204 didnot indicate preferences or that seeking user 204 provided insufficientor conflicting information to establish or calculate normalized weights,the customized user profile 1004 may be augmented by augmentingpreferences of seeking user 204 using reasoning processes. For example,evidence-based reasoning may be utilized to identify and includepreferences of other users that provided similar inputs stored in adatabase of customized user profiles 1010 and identifying a subset ofsimilar mapped attributes and rank ordering to the mapped attributesbased on relative weights of the subset of similar attributes. In theevent that customized user profile 1004 or the database of customizeduser profiles 1010 contains insufficient data to establish a completeand consistent weighted rank ordering, the automated preference rankingengine 1002 may use a discriminating power score of mapped attributes toestablish an initial estimate of the rank ordering. The discriminatingpower score may be used to include and order attributes for inclusion assupporting criteria for the customized user profile for seeking user204. In this manner, each attribute of the supporting criteria has anassociated weight derived from discriminating power of particularfactors, such as, for example, user selection frequency, listinginclusion frequency, perceived value, as well as one or more classes,such as, for example, quality of life, effective age, and proximity.

In some embodiments, customer profile augmentation includes weightedattributes that reflect implied customer interests and theircontribution to the real estate evaluation score. In some embodiments,other user 1014 may be asked to review the results and a reasoningprocess, such as, for example, evidence-based reasoning, is used toidentify factors for meeting the best and highest use of the propertyfor seeking user 204. The review results may be analyzed for specificattributes that can be added to customize the user profile of seekinguser 204 and add new attributes to attribute database 1008 that were notalready recognized or included in data provided by others. The reviewresults may be organized using a subset of factors minimizing thefeedback information requested from seeking user 204 such that thefeedback information provides maximum discriminating power to updatepreference weights. The smaller subset of highly impactful attributesmay ensure that the additional weight will be more influential insubsequent searches.

In some embodiments, the weight evaluation process uses the preferenceweights to identify real estate 1016 and receive feedback from seekinguser 204 and/or other user 1014. In some embodiments, seeking user 204may be the same as other user 1014. In other embodiments, other user1014 may be one or more non-professional individuals, or an informal orformal group of non-professional individuals, or a professional, such asa real estate broker, real estate appraiser, mortgage broker, homerepair technician, or a business employing such professionals, or anyother individual or business providing services to assist seeking user204 in arriving at weighted preferences that achieve best and highestreal estate use for seeking user 204. The personal real estate systemmay compare the weighted attributes to available real estate anddetermine highest and best use for seeking user 204. Seeking user 204and/or other user 1014 may provide feedback and the cycle continues. Thecycle of automated preference ranking, identification of comparable realestate 1016, and user feedback 1018, may continue until seeking user 204signals satisfaction, indicates a desire to conclude the evidence-basedreview process, or when the uncertainty ranges for the customizedprofile elements reach acceptable bounds.

FIG. 11 depicts an exemplary process for ranking user preferencesrepresented generally by the reference numeral 1100. At step 1102, userdata may be collected on primary attributes 1104 associated with seekinguser 204. At step 1108, it may be determined whether sufficient data isavailable to sort primary attributes based on desirability. At step1108, if it is determined that sufficient data is available then theprocess moves to step 1110 where primary attributes are sorted accordingto relative desirability stated by seeking user 204. If, at step 1108,it is determined that insufficient data is available, at step 1112, arequest to provide additional data is provided to seeking user 204. Ifseeking user 204 provides additional data, then the process proceedswith step 1102. If seeking user 204 is unwilling to provide more data,then the process may proceed with step 1114 to collect data on relativedesirability of primary attributes 1104 available from other users. Inthe event that no desirability data is available for a primaryattribute, the primary attributes 1104 may be assigned negligibledesirability.

Furthermore, data may be collected from other users 1120 to completeattribute ranking. In some embodiments, at step 1116, user data may becollected on secondary attributes 1118 associated with other users 1120.It may be valuable to display to seeking user 204 attributes that aredesirable to other users as seeking user 204 may not be aware or mayforget to mention particular attributes. Seeking user 204 may indicatethat they like, dislike, or are indifferent when presented withsecondary attributes 1118. The user data may be updated with theresponse from the seeking user 204 to better match real estate to theattributes of seeking user 204. At step 1122, it may be determinedwhether sufficient data is available to sort secondary attributes basedon desirability. If it is determined, at step 1122, that sufficient datais available, then, at step 1124, secondary attributes 1118 may besorted according to the stated relative desirability of other users1120. If, at step 1122, it is determined that insufficient data isavailable, at step 1126, a request may be provided to other users 1120to determine whether other users 1120 are willing to provide more data.If other users 1120 are willing to provide more data, then the processmay proceed to step 1116. If other users 1120 are unwilling to providemore data, the process may proceed to step 1128 where a negligibledesirability may be assigned a to secondary attributes for which nodesirability data is available from other users 1120. At step 1130, thesorted list of secondary attributes may be appended to the list ofprimary attributes so that primary attributes are ranked higher thansecondary attributes based on the input received from seeking user 204and other users 1120.

FIG. 12 depicts an exemplary process for identifying a set of comparablereal estate properties represented generally by the reference numeral1200. Initially, at step 1202, a weight may be assigned to eachattribute in the collection of primary and secondary attributes based onthe relative desirability of each attribute as described above. At step1204, a sampling of real estate properties may be selected from publicand private databases as described above. At step 1206, the aggregatescore for each real estate property selected at step 1204 may becomputed using the process depicted in FIG. 15 and described in detailbelow. At step 1208 a set of real estate properties may be selected thathave comparable scores to the scores determined at step 1206. At step1210, primary attributes may be identified in the set of propertiesselected in step 1208 where multiple properties in the set have primaryattributes in common and the primary attributes make a significantcontribution in the aggregate score of at least one property in the set.At step 1212, a subset of properties may be selected that have primaryattributes in common and that have the most distinct aggregate scorecontribution in the set. At step 1214, secondary attributes may beidentified in the set of properties selected in step 1208 that multipleproperties in the set have secondary attributes in common and thesecondary attributes make a significant contribution in the aggregatescore of at least one property in the set. At step 1216, a subset ofproperties may be selected that have secondary attributes in common andthat have the most distinct aggregate score contribution in the set. Atstep 1218, the property subsets selected in steps 1212 and 1216 may becombined so that the reduced set contains properties that haveattributes with maximum discriminating power.

FIG. 13 depicts an exemplary process for inviting a user to review a setof comparable real estate properties that were determined in the processdepicted in FIG. 12 above and generally referenced by numeral 1300. Atstep 1302, a request may be presented to seeking user 204 to rate eachproperty in the set of properties with comparable aggregate scores 1304that were determined by the process in FIG. 12 . Seeking user 204 mayrate each property taking into account all attributes of each property.At step 1306, a request may be presented to seeking user 204 to rateeach most distinct primary attribute in the subset of attributes incommon that have maximum discriminating power. At step 1308, seekinguser 204 may then rate said primary attributes in isolation, and, atstep 1310, seeking user may rate secondary attributes in isolation. Atstep 1312, rated secondary attributes may be redesignated as primaryattributes. At step 1314, the aforementioned ratings may be collectedand made available to the method depicted in FIG. 10 as generallydescribed above.

FIG. 14 depicts an exemplary process for obtaining data indicative ofthe real estate property to meet the needs of a user representedgenerally by the reference numeral 1400. At step 1402, all attributes ofreal estate property 1404 from a plurality of data sources areidentified. For example, real estate listing services may providehistorical information entered by real estate professionals indicativeof real estate property which, in some embodiments, may be a home. Thehistorical information may comprise a year built, a size, a number ofrooms, and a type of rooms of the home. Public records such as taxrecords and deed recordings can provide information about the property.Public information may be received and may comprise geographic locationwith respect to roads, homes, businesses, parks, and public servicessuch as water treatment plants. In some embodiments, user providedpictures can be used to extrapolate attributes, and user suppliedinformation can provide attribute information.

At step 1406, weighted attributes are selected that are indicative ofuser's needs 1408. User's needs 1408 may be the explicit needs andimplicit needs of seeking user 204 as described in embodiments above. Atstep 1410, the attributes identified in step 1412, step 1414, and step1416 may be retained for evaluating real estate property 1404 based onuser's needs 1408. At step 1412, attributes of real estate property 1404that map to user's needs 1408 may be identified. At step 1414,attributes may be identified that, from historic data, open-source data,or data indicative of other users, are generally considered desirable orundesirable and that do not map to any attribute characterizing theuser's explicit needs and implicit needs for real estate use or benefit.For example, schools within a determined proximity may be desirablebased on having children that may attend the school or may be anundesirable attribute if the seeking user 204 does not have children,works at home, and desires a quiet neighborhood during the day. At step1416, attributes may be identified that are material facts of a realestate property and that do not map to attributes associated with user'sneeds 1408. Herein, a material fact is any item associated with realestate property 1404 that may change the mind of seeking user 204 aboutbuying real estate property 1404. For example, seeking user 204 may notaddress public service or public use sites such as, for example, alandfill; however, a nearby landfill may influence a decision of seekinguser 204. Attributes that map to material facts may be included for theuser's rating of desirability and may be presented to seeking user 204for personal evaluation.

FIG. 15 depicts an exemplary process of calculating a single score for areal estate property based on preferences of seeking user 204, generallyreferenced by the numeral 1500. In some embodiments, collection ofprimary attributes 1502 and collection of secondary attributes 1504 ofreal estate property 1404 as determined in the process depicted in FIG.14 may be mapped to evaluation criteria 1508. Each attribute in thecustomized user profile 1004 may be associated with at least oneevaluation factor depicted in evaluation criteria 1508. Evaluationfactors may be a single criterion, such as for example evaluation factor1510. In some embodiments, evaluation factor 1510 matches thecorresponding attribute score to receive a contribution of one unit.Multiple criteria, such as for example 1512, must match at least onecorresponding attribute score to receive a contribution of one unit, ora probability distribution of numeric criteria, such as for example1514, that receives a contribution equal to the probability distributionmultiplied by the corresponding attribute score.

In some embodiments, each attribute in collection of primary attributes1502 and collection of secondary attributes 1504 of real estate property1404 may be assigned a single numeric score based on how well theattribute conforms to the metrics that indicate desirability. Forexample, attribute 1516 can have a metric of “unfulfilled”, “partiallyfulfilled” or “fulfilled”. The “fulfilled” state may not change nomatter how many instances of the attribute there are. As an example, theattribute may be a 2-car garage. If real estate property 1404 includesno garage, the awarded value may be 0. If real estate property 1404includes a 1-car garage, the attribute may be awarded a value of 1. Ifreal estate property 1404 includes a 2-car garage, the attribute may beawarded value is 2 and if real estate property 1404 includes a 3-cargarage, the attribute awarded value remains 2. In the case of primaryattribute 1518, the attribute metric is “not present”, “partiallymeeting the goal”, “fully meeting the goal”, and “too many instances ofthe attribute present”, with corresponding desirability. An example ofthis may be a user that desires real estate property 1404 which is adown-town real estate property. In this case the attribute of a numberof parking garages in proximity to real estate property 1404 may beprimary attribute 1502. If there is 1 parking garage within a half-mileof real estate property 1404, the attribute value may decline becausethe specification was for at least 2, not 1 parking garage.

In some embodiments, the attributes, now prioritized by desirability,are associated with imputed weighted scores 1520. In some embodiments,the attributes, prioritized and weighted, may be further analyzed andcombined into relevant groups of attributes by a decision network 1522.Decision network 1522 may be based on machine learning and artificialintelligence tools to create scoring that employs probability to predictuser satisfaction. Decision network 1522 can result in a plurality ofcomponent scores 1524 that are combined in a single score 1528. Forexample, decision network 1522 can determine the contributions ofprimary and secondary scores separately.

FIG. 16 depicts an exemplary process for calculating the relativenon-monetary value to a user seeking a real estate transactionrepresented generally by the reference numeral 1600. Initially, at step1602, aggregate score of real estate property 1404 is calculated asdepicted in FIG. 15 . The calculated aggregate score may be indicativeof the ability of real estate property 1404 to meet the user's realestate needs. At step 1604, a maximum achievable aggregate score may becalculated for a hypothetical ideal real estate property that optimallymeets the user's real estate needs. At step 1606, the aggregate scorecalculated at step 1602 may be divided by the maximum achievableaggregate score calculated at step 1604. The result of the operation atstep 1606 may be the relative non-monetary value to a user seeking(i.e., seeking user 204) a real estate transaction and represents theprobability that the transaction meets the needs of the seeking user204. Seeking user 204 may opt to use the relative non-monetary value toautomatically screen offered transactions, and thus limit the number oftransactions offered for review, or to be notified only when a newlyoffered real estate transaction meets a probability threshold.

Turning now to FIG. 17 , a schematic depiction of a personal real estatesystem 1702 is depicted and referred to generally by reference numeral1700. As depicted, personal real estate system 1702 may assist user 1704among a pool of users 1706 interested in purchasing, leasing, using orotherwise occupying real estate, in identifying real estate property1708 among a pool of real estate properties 1710, that achieves best andhighest real estate use for user 1704 based on the customized profile1716 of user 1704. In some embodiments, user 1704 is seeking user 204.User 1704 may provide user data to personal real estate system 1702 andpersonal real estate system 1702 may determine the needs and attributesand optimize a list of real estate properties that meets the highest andbest use for user 1704 as described in embodiments above.

In some embodiments, personal real estate system 1702 may assistoffering user 1712 among a pool of offering users 1714 interested inoffering to sell, rent or otherwise occupy real estate in identifyingcustomized profiles 1716 among the pool of customized profiles 1716 thatachieves best and highest real estate use for user 1704 based on thecustomized profiles 1716 of user 1704. In some embodiments, offeringuser 1712 may be a real estate agent and may access and provide customoptions to user 1704 based on the received information from personalreal estate system 1702 as described in embodiments above.

In some embodiments, user 1704 or offering user 1712 may seek theadvice, assistance, and/or any other service from user 1718 among a poolof service providers 1720 that facilitates, advises on, or otherwiseaccommodates a transaction between user 1704 and offering user 1712. Theservice provider may have intimate knowledge of personal real estatesystem 1702 and provide updates and maintenance and general assistanceto user 1704 and offering user 1712. In some embodiments, user 1718among the pool of service providers 1720 may offer advice, assistance,and/or any other service to user 1704 among the pool of users 1706 or tooffering user 1712 among the pool of offering users 1714, to facilitate,advise on or otherwise accommodate a transaction between user 1718 anduser 1704 and offering user 1712 individually, separately, or incombination.

In some embodiments, the personal real estate system 1702 may facilitatedirect and unencumbered communication between users, businesses,professional and non-professional users, taking into accountpreferences, permissions and privacy controls set by each user.

Turning now to FIG. 18 , where a process for matching real estateproperties with a user is depicted and referred to generally byreference numeral 1800. The process uses the score of each real estateproperty 1802 in the real estate collection 1804 of real estateproperties offered for sale, rent, or occupation based on the customizedprofile of seeking user 204 and optimization as described in embodimentsabove. A rank ordered list 1808 of real estate properties may begenerated in the real estate collection 1804 such that a subset 1810 ofreal estate properties at top of rank ordered list 1808 achieves thehighest and best use of real estate currently offered for seeking user204 as described in the optimization processes described above. In someembodiments, seeking user 204 may set a threshold 1812 for the size ofsubset 1810, the minimum listing score, or any other criteria thatlimits the set of real estate properties offered to seeking user 204 forreview. Threshold 1812 may be set by seeking user 204 or offering user1712 such that they are not overwhelmed by the size of rank ordered list1808.

FIG. 19 depicts an exemplary process connecting a user seeking a realestate transaction to users offering a real estate transaction thatmeets a seeking user's threshold for the relative non-monetary value ofa real estate transaction represented generally by the reference numeral1900. Initially, at step 1902, a user is identified that seeks a realestate transaction. In some embodiments, the user may be seeking user204. At step 1904, an offering user is selected from the pool of users1906 that offer a real estate transaction. In some embodiments, theoffering user may be offering user 1712. At step 1908, the potentialtransaction between seeking user 204 and offering user 1712 is evaluatedas depicted in block 212 in FIG. 2 . At step 1910, a check is performedto determine if the offered transaction meets the threshold of seekinguser 204. If the offered transaction meets the threshold of seeking user204, at step 1912, the offering user 1712 is added to a list of offeringusers. At step 1914, a determination is made by whether to continue theevaluation of more transactions. The determination may be based onstopping criteria specified by seeking user 204 and/or personal realestate system 1702 automating the process. For example, seeking user 204may opt to evaluate all offered real estate transactions or only asampling of transactions, or personal real estate system 1702 mayenforce a maximum time limit for processing transaction evaluations andmake determinations based on the user data of seeking user 204.

Turning now to FIG. 20 , a process for matching a real estate propertyto users is depicted and referred to generally by reference numeral2000. The process may use user scores 2002 of real estate propertylisting 2006 computed using the customized profiles of all individualusers 2004 to generate a rank ordered list of users 2008 interested inreal estate properties offered for sale, rent, or occupation such thatusers subset 2010 at the top of list of users 2008 achieves the highestand best use of real estate property 2006. In some embodiments, an owneror offeror of real estate property 2006 may elect to market real estateproperty 2006 to the subset of users 2008 at the top of rank orderedlist of users 2008. Furthermore, the owner or real estate seller may usethe user scores 2002 to evaluate the desirability or market value ofreal estate property 2006 or determine the change in desirability ormarket value of real estate property 2006 after making modifications,additions, improvements, or any other changes to the characteristics. Insome embodiments, owner of real estate property 2006 may set a userthreshold 2012 for the size of user subset 2010, the minimum user score,or any other criteria that limits the subset of users provided to theowner or offeror of real estate property 2006 for review or marketing.The real estate seller, or an offering user, may update the real estateproperty listing 2006 based on the recognized attributes of subset ofuser 2008 to market to specific users. In some embodiments, the offeringuser may update real estate property listing 2006 to market to a broaderlist of users based on seeking user attributes and informationindicative of seeking user profiles presented the offering user.

FIG. 21 depicts an exemplary process connecting a user offering a realestate transaction to users seeking a real estate transaction that meetsa threshold for seeking user 204 for the relative non-monetary value ofa real estate transaction represented generally by the reference numeral2100. At step 2102, an offering user is identified that offers a realestate transaction. At step 2104, seeking user 204 is selected from thepool of users 2106 that seek a real estate transaction. At step 2108,the potential transaction between offering user 1712 and seeking user204 may be evaluated as depicted in block 201 in FIG. 2 . At step 2110,a check may be performed to determine if the offered transaction meetsthe threshold of seeking user 204. If the offered transaction meets thethreshold, at step 2112, seeking user 204 may be added to a list ofpotential buyers. At step 2114, a determination is made whether tocontinue the evaluation of more transactions. The determination may bebased on stopping criteria specified by offering user 1712 or personalreal estate system 1702 automating the process. For example, offeringuser 1712 may opt to evaluate all seeking real estate transactions oronly a sampling of transactions, or personal real estate system 1702 mayenforce a maximum time limit for processing transaction evaluations.Offering user may receive information indicative of attributesassociated with seeking user 204 and matching real estate that offeringuser is selling such that offering user may contact seeking user 204 topresent real estate options.

Turning now to FIG. 22 , a flowchart illustrating the operation of amethod of generating customized reports 2202 is depicted and referred togenerally by reference numeral 2200. In some embodiments, seeking user204 is assisted in evaluating current or future real estate andassociated transactions for achieving best and highest real estate use.In some embodiments, seeking user 204 engages with user interface 408 tocollect and update customized user profiles 402 of seeking user 204. Insome embodiments, real estate profiler 406 compares customized userprofiles 402 with profiles of other users stored in database of otheruser profiles 426, past and current real estate listings stored in realestate database 424, and real estate attributes 428 that map elements ofcustomized user profiles 402 to features of past and current real estatelistings stored in real estate database 424 to generate customizedreports 2202 for seeking user 204. In some embodiments, user 204 mayreview customized reports 2202 through user interface 408 and may opt toadd, remove, and/or update customized user profiles 402 to broaden ornarrow the information provided by customized reports 2202. Furthermore,seeking user 204 may recognize and add information about the real estatelisted in customized reports 2202 through personal real estate system1702 processes, and/or may opt to pass on to, or share, with other user1014 at least one customized report or seek the advice or services ofother user 1014 or initiate other transactions related to the realestate listed in the results of customized reports 2202.

In some embodiments, customized reports 2202 may include an estimate ofthe relative market value or relative market rent of real estate owned,rented, or occupied by seeking user 204 based on the desirability ofattributes identified in real estate owned by seeking user 204 asdetermined by the real estate profiler 406 based on user preferencesobserved in customized user profiles 402 and on the prevalence orscarcity of the desirable attributes identified in real estate owned byseeking user 204. In some embodiments, the customized reports 2202 mayinclude comparable real estate properties that apply the estimatedrelative market value or market rent in perspective.

In some embodiments, customized reports 2202 may include information toassist seeking user 204 in making a stay-or-go decision. In someembodiments, real estate profiler 406 uses at least one customized userprofile of seeking user 204 to identify real estate for sale,occupation, or rent that achieves higher and best use than the realestate currently owned, occupied, or rented by seeking user 204.

In some embodiments, customized reports 2202 may inform seeking user 204on local trends in real estate attributes that may make real estateowned, rented, or occupied by seeking user 204 more or less desirable.In some embodiments, customized reports 2202 may assist seeking user 204in choosing upgrades in real estate owned by seeking user 204 such asremodeling, and additions that have the best return in investment interms of market value or that are an economical alternative to sellingreal estate owned by user 204 to achieve highest and best value.

In some embodiments, other user 1014 may be a user who owns or desiresto own real estate or leases or desires to rent real estate or occupiesor desires to occupy real estate. Other user 1014 may also be aprofessional, such as a real estate broker, real estate appraiser,mortgage broker, home repair technician, or any other individualproviding services to assist seeking user 204 in achieving best andhighest real estate use, or a business employing such professionals.

Many different arrangements of the various components depicted, as wellas components not shown, are possible without departing from the scopeof the claims below. Embodiments of the invention have been describedwith the intent to be illustrative rather than restrictive. Alternativeembodiments will become apparent to readers of this disclosure after andbecause of reading it. Alternative means of implementing theaforementioned can be completed without departing from the scope of theclaims below. Certain features and subcombinations are of utility andmay be employed without reference to other features and subcombinationsand are contemplated within the scope of the claims. Although theinvention has been described with reference to the embodimentsillustrated in the attached drawing figures, it is noted thatequivalents may be employed, and substitutions made herein withoutdeparting from the scope of the invention as recited in the claims.

Having thus described various embodiments of the invention, what isclaimed as new and desired to be protected by Letters Patent includesthe following:

1. A method of determining non-monetary value of a real estatetransaction and matching real estate property to attributes of a realestate seeker, the method comprising: obtaining user data comprisingexplicit needs of the real estate seeker; determining implicit needs ofthe real estate seeker based at least in part on the user data;determining a plurality of attributes from the explicit needs and theimplicit needs; ranking the plurality of attributes based on at leastone of the explicit needs or the implicit needs of the real estateseeker; determining a ranked order of a plurality of real estateproperties based on the ranking of the plurality of attributes;obtaining feedback from the real estate seeker based on the plurality ofreal estate properties; and updating the ranked order of the attributesto minimize additional feedback from the real estate seeker.
 2. Themethod of claim 1, wherein the plurality of real estate properties arecommercial properties.
 3. The method of claim 1, further comprisingdetermining a plurality of secondary attributes from a primary attributeof the plurality of attributes, wherein the plurality of secondaryattributes is dependent upon the primary attribute.
 4. The method ofclaim 3, wherein the primary attribute is a location associated with aproperty of the plurality of real estate properties, and the pluralityof secondary attributes are living conditions dependent upon thelocation.
 5. The method of claim 4, further comprising obtaining thelocation from a geographic feature database comprising a plurality ofgeographic location databases; and selecting a location with reducednoise from a plurality of geographic locations from the plurality ofgeographic location databases.
 6. The method of claim 1, wherein aproperty of the real estate seeker is one of the plurality of realestate properties.
 7. The method of claim 1, further comprising reducinga number of the ranked order of the plurality of real estate propertiesby maximizing discriminating power of real estate seeker preferences. 8.The method of claim 1, further comprising matching the plurality ofattributes to real estate attributes by extrapolating and classifyingthe real estate attributes from images of the plurality of real estateproperties.
 9. One or more non-transitory computer-readable mediastoring computer-executable instructions that, when executed by at leastone processor, perform a method of matching real estate property toattributes of a real estate seeker, the method comprising: obtaininguser data comprising explicit needs of the real estate seeker;determining implicit needs of the real estate seeker based at least inpart on the user data; receiving real estate seeker preferences;determining a plurality of attributes from the explicit needs and theimplicit needs; ranking the plurality of attributes based on at leastone of the explicit needs or the implicit needs of the real estateseeker; determining a ranked order of a plurality of real estateproperties based on the ranking of the plurality of attributes; andreducing a number of the ranked order of the plurality of real estateproperties by maximizing discriminating power of the real estate seekerpreferences.
 10. The media of claim 9, wherein a property of the realestate seeker is one of the plurality of real estate properties.
 11. Themedia of claim 9, the method further comprising: weighting theattributes based on the real estate seeker preferences to obtainweights; and ranking the attributes based on the weights.
 12. The mediaof claim 11, the method further comprising: receiving feedback from thereal estate seeker based on the plurality of real estate properties;updated the weights based on the feedback from the real estate seeker toobtain updated weights; and reranking the attributes based on theupdated weights.
 13. The media of claim 11, the method furthercomprising: comparing the real estate seeker preferences to other userpreferences, wherein the other user preferences are similar to the realestate seeker preferences; updating the weights based on the other userpreferences to obtain updated weights; and reranking the attributesbased on the updated weights.
 14. The media of claim 9, the methodfurther comprising: requesting feedback from the real estate seekerbased on the plurality of real estate properties; and updating theranked order of the attributes to minimize additional feedback from thereal estate seeker.
 15. A method of determining non-monetary value of areal estate transaction and matching real estate property to attributesof a real estate seeker, the method comprising: obtaining user datacomprising explicit needs of the real estate seeker; determiningimplicit needs of the real estate seeker based at least in part on theuser data; determining a plurality of attributes from the explicit needsand the implicit needs; receiving real estate seeker preferences;ranking the plurality of attributes based on at least one of theexplicit needs or the implicit needs of the real estate seeker;determining a ranked order of a plurality of real estate propertiesbased on the ranking of the plurality of attributes; obtaining feedbackfrom the real estate seeker based on the plurality of real estateproperties; updating the ranked order of the attributes to minimizeadditional feedback from the real estate seeker; and reducing a numberof the ranked order of the plurality of real estate properties bymaximizing discriminating power of the real estate seeker preferences.16. The method of claim 15, further comprising matching the plurality ofattributes to real estate attributes by extrapolating and classifyingthe real estate attributes from images of the plurality of real estateproperties.
 17. The method of claim 15, wherein the plurality of realestate properties are commercial properties.
 18. The method of claim 15,further comprising: weighting the attributes based on the real estateseeker preferences to obtain weights; and ranking the attributes basedon the weights.
 19. The method of claim 15, wherein a property of thereal estate seeker is one of the plurality of real estate properties.20. The method of claim 15, further comprising: obtaining a location ofa property of the plurality of real estate properties from a geographicfeature database comprising a plurality of geographic locationdatabases; and selecting a location with reduced noise from a pluralityof geographic locations from the plurality of geographic locationdatabases.